Counts

Happy Families are All Alike

Abstract

Quant and Qual Variable Graphs and their Siblings

Slides and Tutorials

“No matter what happens in life, be good to people. Being good to people is a wonderful legacy to leave behind.”

— Taylor Swift

Setting up R Packages

library(tidyplots) # Easily Produced Publication-Ready Plots
library(tinyplot) # Plots with Base R
library(tinytable) # Elegant Tables for our data
library(mosaic)
library(skimr)
library(ggformula)
library(tidyverse) # Most Important Last

Plot Fonts and Theme

library(systemfonts)
library(showtext)
## Clean the slate
systemfonts::clear_local_fonts()
systemfonts::clear_registry()
##
showtext_opts(dpi = 96) # set DPI for showtext
sysfonts::font_add(
  family = "Alegreya",
  regular = "../../../../../../fonts/Alegreya-Regular.ttf",
  bold = "../../../../../../fonts/Alegreya-Bold.ttf",
  italic = "../../../../../../fonts/Alegreya-Italic.ttf",
  bolditalic = "../../../../../../fonts/Alegreya-BoldItalic.ttf"
)

Error in check_font_path(bold, "bold"): font file not found for 'bold' type

sysfonts::font_add(
  family = "Roboto Condensed",
  regular = "../../../../../../fonts/RobotoCondensed-Regular.ttf",
  bold = "../../../../../../fonts/RobotoCondensed-Bold.ttf",
  italic = "../../../../../../fonts/RobotoCondensed-Italic.ttf",
  bolditalic = "../../../../../../fonts/RobotoCondensed-BoldItalic.ttf"
)
showtext_auto(enable = TRUE) # enable showtext
##
theme_custom <- function() {
  font <- "Alegreya" # assign font family up front

  theme_classic(base_size = 14, base_family = font) %+replace% # replace elements we want to change

    theme(
      text = element_text(family = font), # set base font family

      # text elements
      plot.title = element_text( # title
        family = font, # set font family
        size = 24, # set font size
        face = "bold", # bold typeface
        hjust = 0, # left align
        margin = margin(t = 5, r = 0, b = 5, l = 0)
      ), # margin
      plot.title.position = "plot",
      plot.subtitle = element_text( # subtitle
        family = font, # font family
        size = 14, # font size
        hjust = 0, # left align
        margin = margin(t = 5, r = 0, b = 10, l = 0)
      ), # margin

      plot.caption = element_text( # caption
        family = font, # font family
        size = 9, # font size
        hjust = 1
      ), # right align

      plot.caption.position = "plot", # right align

      axis.title = element_text( # axis titles
        family = "Roboto Condensed", # font family
        size = 12
      ), # font size

      axis.text = element_text( # axis text
        family = "Roboto Condensed", # font family
        size = 9
      ), # font size

      axis.text.x = element_text( # margin for axis text
        margin = margin(5, b = 10)
      )

      # since the legend often requires manual tweaking
      # based on plot content, don't define it here
    )
}

```{r}
#| cache: false
#| code-fold: true
## Set the theme
theme_set(new = theme_custom())

## Use available fonts in ggplot text geoms too!
update_geom_defaults(geom = "text", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
```

What graphs will we see today?

Variable #1	Variable #2	Chart Names	Chart Shape
Qual	None	Bar Chart

What kind of Data Variables will we choose?

No	Pronoun	Answer	Variable/Scale	Example	What Operations?
3	How, What Kind, What Sort	A Manner / Method, Type or Attribute from a list, with list items in some " order" ( e.g. good, better, improved, best..)	Qualitative/Ordinal	Socioeconomic status (Low income, Middle income, High income),Education level (HighSchool, BS, MS, PhD),Satisfaction rating(Very much Dislike, Dislike, Neutral, Like, Very Much Like)	Median,Percentile

Inspiration

Figure 1: Capital Cities

How much does the (financial) capital of a country contribute to its GDP? Which would be India’s city? What would be the reduction in percentage? And these Germans are crazy.(Toc, toc, toc, toc!)

Note how the axis variable that defines the bar locations is a …Qual variable!

Graphing Packages in R

There are several Data Visualization packages, even systems, within R.

• Base R supports graph making out of the box; (fast, very flexible, but a bit complex)(Update July 2025: There is a new(?) package called tinyplots that creates base R plots with syntax that is much more intuitive. (We will include some here for reference.)

• The most well known is ggplot https://ggplot2-book.org/ which uses Leland Wilkinson’s concept of a “Grammar of Graphics”; ggformula is a wrapper around ggplot that makes the syntax a little more concise and intuitive.

• There is the lattice package https://lattice.r-forge.r-project.org/ which uses the “Trellis Graphics” concept framework for data visualization developed by R. A. Becker, W. S. Cleveland, et al.;

• And the grid package https://bookdown.org/rdpeng/RProgDA/the-grid-package.html that allows extremely fine control of shapes plotted on the graph.

Each system has its benefits and learning complexities. We will look at plots created using ggformula, and the recently introduced tidyplots package, which allows intuitive creation of publication-ready charts based on the famous and established ggplot framework. We will, where appropriate state ggplot code too for comparison.

A quick reminder on how mosaic and ggformula and ggplot work in a very similar fashion:

mosaic and ggformula command template

Note the standard method for all commands from the mosaic and ggformula packages: goal( y ~ x | z, data = _____)

With mosaic, one can create a statistical correlation test between two variables as: cor_test(y ~ x, data = ______ )

With ggformula, one can create any graph/chart using: gf_***(y ~ x | z, data = _____) In practice, we often use: dataframe %>% gf_***(y ~ x | z) which has cool benefits such as “autocompletion” of variable names, as we shall see. The “***” indicates what kind of graph you desire: histogram, bar, scatter, density; the “___” is the name of your dataset that you want to plot with.

ggplot command template

The ggplot template is used to identify the dataframe, identify the x and y axis, and define visualized layers:

ggplot(data = ---, mapping = aes(x = ---, y = ---)) + geom_----()

Note: —- is meant to imply text you supply. e.g. function names, data frame names, variable names.

It is helpful to see the argument mapping, above. In practice, rather than typing the formal arguments, code is typically shorthanded to this:

dataframe %>% ggplot(aes(xvar, yvar)) + geom_----()

tidyplots command template

tidyplot(data = ---, x = ---, y = ---, color = ---) |> add_***_***()

Bar Charts and Histograms

Bar Charts show counts of observations with respect to a Qualitative variable. For instance, a shop inventory with shirt-sizes. Each bar has a height proportional to the count per shirt-size, in this example.

Although Histograms may look similar to Bar Charts, the two are different. First, histograms show continuous Quant data. By contrast, bar charts show categorical data, such as shirt-sizes, or apples, bananas, carrots, etc. Visually speaking, histograms do not usually show spaces between bars because these are continuous values, while column charts must show spaces to separate each category.

How do Bar Chart(s) Work?

Bar are used to show “counts” and “tallies” with respect to Qual variables: they answer the question How Many?. For instance, in a survey, how many people vs Gender? In a Target Audience survey on Weekly Consumption, how many low, medium, or high expenditure people?

Each Qual variable potentially has many levels as we saw in the Nature of Data. For instance, in the above example on Weekly Expenditure, low, medium and high were levels for the Qual variable Expenditure. Bar charts perform internal counts for each level of the Qual variable under consideration. The Bar Plot is then a set of disjoint bars representing these counts; see the icon above, and then that for histograms!! The X-axis is the set of levels in the Qual variable, and the Y-axis represents the counts for each level.

Case Study-1: Chicago Taxi Rides dataset

We will first look at at a dataset that speaks about taxi rides in Chicago in the year 2022. This is available on Vincent Arel-Bundock’s superb repository of datasets.Let us read into R directly from the website.

R

taxi <- read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/modeldata/taxi.csv")

The data has automatically been read into the webr session, so you can continue on to the next code chunk!

Examine the Data

As per our Workflow, we will look at the data using all the three methods we have seen.

dplyr

dplyr::glimpse(taxi)

Rows: 10,000
Columns: 8
$ rownames <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ tip      <chr> "yes", "yes", "yes", "yes", "yes", "yes", "yes", "yes", "yes"…
$ distance <dbl> 17.19, 0.88, 18.11, 20.70, 12.23, 0.94, 17.47, 17.67, 1.85, 1…
$ company  <chr> "Chicago Independents", "City Service", "other", "Chicago Ind…
$ local    <chr> "no", "yes", "no", "no", "no", "yes", "no", "no", "no", "no",…
$ dow      <chr> "Thu", "Thu", "Mon", "Mon", "Sun", "Sat", "Fri", "Sun", "Fri"…
$ month    <chr> "Feb", "Mar", "Feb", "Apr", "Mar", "Apr", "Mar", "Jan", "Apr"…
$ hour     <dbl> 16, 8, 18, 8, 21, 23, 12, 6, 12, 14, 18, 11, 12, 19, 17, 13, …

skimr

skimr::skim(taxi)

Data summary

Name	taxi
Number of rows	10000
Number of columns	8
_______________________
Column type frequency:
character	5
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
tip	0	1	2	3	0	2	0
company	0	1	5	28	0	7	0
local	0	1	2	3	0	2	0
dow	0	1	3	3	0	7	0
month	0	1	3	3	0	4	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
rownames	0	1	5000.50	2886.90	1	2500.75	5000.50	7500.25	10000.0	▇▇▇▇▇
distance	0	1	6.22	7.38	0	0.94	1.78	15.56	42.3	▇▁▂▁▁
hour	0	1	14.18	4.36	0	11.00	15.00	18.00	23.0	▁▃▅▇▃

mosaic

mosaic::inspect(taxi)

categorical variables:  
     name     class levels     n missing
1     tip character      2 10000       0
2 company character      7 10000       0
3   local character      2 10000       0
4     dow character      7 10000       0
5   month character      4 10000       0
                                   distribution
1 yes (92.1%), no (7.9%)                       
2 other (27.1%) ...                            
3 no (81.2%), yes (18.8%)                      
4 Thu (19.6%), Wed (17.5%), Tue (16.3%) ...    
5 Apr (31.8%), Mar (31.4%), Feb (20.4%) ...    

quantitative variables:  
      name   class min      Q1  median        Q3     max        mean
1 rownames numeric   1 2500.75 5000.50 7500.2500 10000.0 5000.500000
2 distance numeric   0    0.94    1.78   15.5625    42.3    6.224144
3     hour numeric   0   11.00   15.00   18.0000    23.0   14.177300
           sd     n missing
1 2886.895680 10000       0
2    7.381397 10000       0
3    4.359904 10000       0

web-r

Data Dictionary

Quantitative Data

• distance: Continuous Quant variable, the distance of the trip in miles.

Qualitative Data

• tip: Yes/No type Qual variable, whether a tip was given or not.
• company: 7 levels, the cab company that was used for the ride.
• local: 2 levels, whether the trip was local or not.
• hour : 24 levels, the hour of the day when the trip started.
• dow: 7 levels, the day of the week.
• month: 12 levels, the month of the year.

Business Insights on Examining the taxi dataset

• This is a large dataset (10K rows), 8 columns/variables.
• There are several Qualitative variables: tip(2), company(7) and local(2), dow(7), and month(12). These have levels as shown in the parenthesis.
• Note that hour despite being a discrete/numerical variable, it can be treated as a Categorical variable too.
• distance is Quantitative.
• There are no missing values for any variable, all are complete with 10K entries.

Data Munging

We will convert the tip, company, dow, local, hour, and month variables into factors beforehand.

## Convert `dow`, `local`, `month`, and `hour` into ordered factors
taxi_modified <- taxi %>%
  mutate(
    ##
    tip = factor(tip,
      levels = c("yes", "no"),
      labels = c("yes", "no"),
      ordered = TRUE
    ),
    ##
    company = factor(company), # Any order is OK.
    ##
    dow = factor(dow,
      levels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"),
      labels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"),
      ordered = TRUE
    ),
    ##
    local = factor(local,
      levels = c("yes", "no"),
      labels = c("yes", "no"),
      ordered = TRUE
    ),
    ##
    month = factor(month,
      levels = c("Jan", "Feb", "Mar", "Apr"),
      labels = c("Jan", "Feb", "Mar", "Apr"),
      ordered = TRUE
    ),
    ##
    hour = factor(hour,
      levels = c(0:23), labels = c(0:23),
      ordered = TRUE
    )
  )
taxi_modified %>% glimpse()

Rows: 10,000
Columns: 8
$ rownames <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ tip      <ord> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, y…
$ distance <dbl> 17.19, 0.88, 18.11, 20.70, 12.23, 0.94, 17.47, 17.67, 1.85, 1…
$ company  <fct> Chicago Independents, City Service, other, Chicago Independen…
$ local    <ord> no, yes, no, no, no, yes, no, no, no, no, no, no, no, yes, no…
$ dow      <ord> Thu, Thu, Mon, Mon, Sun, Sat, Fri, Sun, Fri, Tue, Tue, Sun, W…
$ month    <ord> Feb, Mar, Feb, Apr, Mar, Apr, Mar, Jan, Apr, Mar, Mar, Apr, A…
$ hour     <ord> 16, 8, 18, 8, 21, 23, 12, 6, 12, 14, 18, 11, 12, 19, 17, 13, …

Hypothesis and Research Questions

The target variable for an experiment that resulted in this data might be the tip variable, since that looks like a response, or an outcome. It is a binary i.e. Yes/No type Qual variable.

We will use the tip variable to ask questions about the data, and then plot the answers to these questions.

Research Questions:

1. Do more people tip than not?
2. Does a tip depend upon whether the trip is local or not?
3. Do some cab company-ies get more tips than others?
4. And does a tip depend upon the distance, hour of day, and dow and month?

Try and think of more Questions!

Plotting Barcharts

Let’s plot some bar graphs: recall that for bar charts, we need to choose Qual variables to count with! In each case, we will state a Hypothesis/Question and try to answer it with a chart.

Question-1: Do more people tip than not?

Question-1: Do more people tip than not?

ggformula-1

theme_set(new = theme_custom())

gf_bar(~tip, data = taxi_modified) %>%
  gf_labs(title = "Plot 1A: Counts of Tips")

Figure 2

ggplot-1

theme_set(new = theme_custom())

ggplot(taxi_modified) +
  geom_bar(aes(x = tip)) +
  labs(title = "Plot 1A: Counts of Tips")

Figure 3

tidyplots-1

tidyplot(x = tip, data = taxi_modified) %>%
  add_count_bar() %>%
  add_title("Plot 1A: Counts of Tips") %>%
  adjust_size(height = 50, width = 85, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

Figure 4

tinyplot-1

tinyplot(~tip,
  data = taxi_modified,
  type = "barplot",
  main = "Plot 1A: Counts of Tips"
)

Figure 5

web-r-1

Business Insights-1

• Far more people do tip than not. Which is nice.
• (Future) The counts of tip are very imbalanced and if we are to setup a model for that (logistic regression) we would need to very carefully subset the data for training and testing our model.

Question-2: Does the tip depend upon whether the trip is local or not?

Question-2: Does the tip depend upon whether the trip is local or not?

ggformula-2

theme_set(new = theme_custom())

taxi_modified %>%
  gf_bar(~local,
    fill = ~tip,
    position = "dodge"
  ) %>%
  gf_labs(title = "Plot 2A: Dodged Bar Chart") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

taxi_modified %>%
  gf_bar(~local,
    fill = ~tip,
    position = "stack"
  ) %>%
  gf_labs(
    title = "Plot 2B: Stacked Bar Chart",
    subtitle = "Can we spot per group differences in proportions??"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

## Showing "per capita" percentages
taxi_modified %>%
  gf_bar(~local,
    fill = ~tip,
    position = "fill"
  ) %>%
  gf_labs(
    title = "Plot 2C: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

## Showing "per capita" percentages
## Better labelling of Y-axis
taxi_modified %>%
  gf_props(~local,
    fill = ~tip,
    position = "fill"
  ) %>%
  gf_labs(
    title = "Plot 2D: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

ggplot-2

theme_set(new = theme_custom())

taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = local, fill = tip), position = "dodge") +
  labs(title = "Plot 2A:Dodged Bar Chart") +
  scale_fill_brewer(palette = "Set1")
##
taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = local, fill = tip), position = "stack") +
  labs(
    title = "Plot 2B: Stacked Bar Chart",
    subtitle = "Can we spot per group differences in proportions??"
  ) +
  scale_fill_brewer(palette = "Set1")
## Showing "per capita" percentages
taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = local, fill = tip), position = "fill") +
  labs(title = "Plot 2C: Filled Bar Chart", subtitle = "Shows Per group differences in Proportions!") +
  scale_fill_brewer(palette = "Set1")
## Showing "per capita" percentages
## Better labelling of Y-axis
taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = local, fill = tip), position = "fill") +
  labs(
    title = "Plot 2D: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!",
    y = "Proportion"
  ) +
  scale_fill_brewer(palette = "Set1")

tidyplots-2

tidyplots::tidyplot(local,
  colour = tip,
  data = taxi_modified
) %>%
  add_count_bar() %>%
  add_title("Plot 2A: Dodged Bar Chart") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

tidyplots::tidyplot(local, colour = tip, data = taxi_modified) %>%
  add_barstack_absolute() %>%
  add_title("Plot 2B: Stacked Bar Chart") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

tidyplots::tidyplot(local,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_relative() %>%
  add_title("Plot 2C: Dodged Bar Chart") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

tinyplot-2

tinyplot(~ local | tip,
  data = taxi_modified,
  type = "barplot", palette = "tableau",
  beside = TRUE, # for placing bars beside one another
  main = "Plot 2A: Dodged Bar Chart",
  legend = "right!"
) # Outside, to the right

tinyplot(~ local | tip,
  data = taxi_modified,
  type = "barplot", palette = "tableau",
  beside = FALSE,
  xlevels = c("no", "yes"), # try reversing
  main = "Plot 2B: Stacked Bar Chart",
  sub = "Can we spot per group differences in proportions??",
  legend = "right!"
)

web-r-2

Business Insights-2

• Counting the frequency of tip by local gives us grouped counts, but we cannot tell the percentage per group (local or not) of those who tip and those who do not.
• We need per-group percentages because the number of local trips are not balanced
• Hence with tidyplots, we find that add_barstack_relative gives the clearest visual indication of a difference in proportion.
• Likewise with ggformula, we tried bar charts with position = stack, but finally it is the position = fill that works best.
• We see that the percentage of tippers is somewhat higher with people who make non-local trips. Not surprising.

Question-3: Do some cab company-ies get more tips than others?

Question-3: Do some cab company-ies get more tips than others?

ggformula-3

theme_set(new = theme_custom())

taxi_modified %>%
  gf_bar(~company, fill = ~tip, position = "dodge") %>%
  gf_labs(title = "Plot 3A: Dodged Bar Chart") %>%
  gf_theme(theme(axis.text.x = element_text(
    size = 6,
    angle = 45, hjust = 0.5
  ))) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

taxi_modified %>%
  gf_bar(~company, fill = ~tip, position = "stack") %>%
  gf_labs(
    title = "Plot 3B: Stacked Bar Chart",
    subtitle = "Can we spot per group differences in proportions??"
  ) %>%
  gf_theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

## Showing "per capita" percentages
taxi_modified %>%
  gf_percents(~company, fill = ~tip, position = "fill") %>%
  gf_labs(
    title = "Plot 3C: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!"
  ) %>%
  gf_theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

## Showing "per capita" percentages
## Better labelling of Y-axis
taxi_modified %>%
  gf_props(~company, fill = ~tip, position = "fill") %>%
  gf_labs(
    title = "Plot 3D: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!"
  ) %>%
  gf_theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

ggplot-3

theme_set(new = theme_custom())

taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = company, fill = tip), position = "dodge") +
  labs(title = "Plot 3A: Dodged Bar Chart") +
  theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) +
  scale_fill_brewer(palette = "Set1")
##
taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = company, fill = tip), position = "stack") +
  labs(
    title = "Plot 3B: Stacked Bar Chart",
    subtitle = "Can we spot per group differences in proportions??"
  ) +
  theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) +
  scale_fill_brewer(palette = "Set1")
## Showing "per capita" percentages
taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = company, fill = tip), position = "fill") +
  labs(
    title = "Plot 3C: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!"
  ) +
  theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) +
  scale_fill_brewer(palette = "Set1")
## Showing "per capita" percentages
## Better labelling of Y-axis
taxi_modified %>%
  ggplot() +
  geom_bar(aes(x = company, fill = tip), position = "fill") +
  labs(
    title = "Plot 3D: Filled Bar Chart",
    subtitle = "Shows Per group differences in Proportions!",
    y = "Proportions"
  ) +
  theme(theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1))) +
  scale_fill_brewer(palette = "Set1")

tidyplots-3

tidyplots::tidyplot(company,
  colour = tip,
  data = taxi_modified
) %>%
  add_count_bar() %>%
  add_title("Plot 3A: Dodged Bar Chart") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_x_axis(rotate_labels = 45)

tidyplots::tidyplot(company,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_absolute() %>%
  add_title("Plot 3B: Stacked Bar Chart") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_x_axis(rotate_labels = 45)

tidyplots::tidyplot(company,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_relative() %>%
  add_title("Plot 3C: Stacked Bar Chart") %>%
  add_annotation_text(
    text = "Proportions of Tippers per Company",
    x = 4.25, y = 1.25
  ) %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_x_axis(title = "", rotate_labels = 45) %>%
  adjust_y_axis(title = "Proportions") # Not happening??

tinyplot-3

To be Coded!!

web-r-3

Business Insights-3

• Using stack-ed, dodge-ed, and fill-ed in ggformula (and bars, absolute-stacked-bars, and relative-stacked-bars in tidyplots) in bar plots gives us different ways of looking at the sets of counts;
• fill: gives us a per-group proportion of another Qual variable for a chosen Qual variable. This chart view is useful in Inference for Proportions;
• Most cab company-ies have similar usage, if you neglect the other category of company;
• Does seem that of all the company-ies, tips are not so good for the Flash Cab company. A driver issue? Or are the cars too old? Or don’t they offer service everywhere?

Question-4: Does a tip depend upon the distance, hour of day, and dow and month?

Question-4: Does a tip depend upon the distance, hour of day, and dow and month?

ggformula-4

theme_set(new = theme_custom())

gf_bar(~hour, fill = ~tip, data = taxi_modified) %>%
  gf_labs(title = "Plot 4A: Counts of Tips by Hour") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

gf_bar(~dow, fill = ~tip, data = taxi_modified) %>%
  gf_labs(title = "Plot 4B: Counts of Tips by Day of Week") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

gf_bar(~month, fill = ~tip, data = taxi_modified) %>%
  gf_labs(title = "Plot 4C: Counts of Tips by Month") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

gf_bar(~ month | dow, fill = ~tip, data = taxi_modified) %>%
  gf_labs(title = "Plot 4D: Counts of Tips by Day of Week and Month") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

## This may be too busy a graph...
gf_bar(~ dow | hour, fill = ~tip, data = taxi_modified) %>%
  gf_labs(
    title = "Plot 4E: Counts of Tips by Hour and Day of Week",
    subtitle = "Is this plot arrangement easy to grasp?"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

## This is better!
gf_bar(~ hour | dow, fill = ~tip, data = taxi_modified) %>%
  gf_labs(
    title = "Plot 4F: Counts of Tips by Hour and Day of Week",
    subtitle = "Facetted by Day of Week"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

ggplot-4

theme_set(new = theme_custom())

gf_bar(~hour, fill = ~tip, data = taxi_modified) %>%
  gf_labs(title = "Plot 4A: Counts of Tips by Hour") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))
##
ggplot(taxi_modified) +
  geom_bar(aes(x = dow, fill = tip)) +
  labs(title = "Plot 4B: Counts of Tips by Day of Week") +
  scale_fill_brewer(palette = "Set1")
##
ggplot(taxi_modified) +
  geom_bar(aes(x = month, fill = tip)) +
  labs(title = "Plot 4C: Counts of Tips by Month") +
  scale_fill_brewer(palette = "Set1")
##
ggplot(taxi_modified) +
  geom_bar(aes(x = month, fill = tip)) +
  facet_wrap(~dow) +
  labs(title = "Plot 4D: Counts of Tips by Day of Week and Month") +
  scale_fill_brewer(palette = "Set1")
##
ggplot(taxi_modified) +
  geom_bar(aes(x = dow, fill = tip)) +
  facet_wrap(~hour) +
  labs(
    title = "Plot 4E: Counts of Tips by Hour and Day of Week",
    subtitle = "Is this plot arrangement easy to grasp?"
  ) +
  scale_fill_brewer(palette = "Set1")
##
ggplot(taxi_modified) +
  geom_bar(aes(x = hour, fill = tip)) +
  facet_wrap(~dow) +
  labs(
    title = "Plot 4F: Counts of Tips by Hour and Day of Week",
    subtitle = "Swapped the Facets"
  ) +
  scale_fill_brewer(palette = "Set1")

tidyplots-4

tidyplots::tidyplot(hour,
  colour = tip,
  data = taxi_modified
) %>%
  add_count_bar() %>%
  add_title("Plot 4A: Counts of Tips by Hour") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

tidyplots::tidyplot(hour,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_absolute() %>%
  add_title("Plot 4B: Counts of Tips by Hour") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

tidyplots::tidyplot(hour,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_relative() %>%
  add_title("Plot 4C: Counts of Tips by Hour") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly)

##
tidyplots::tidyplot(month,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_absolute() %>%
  add_title("Plot 4D: Counts of Tips by Day of Week and Month") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly) %>%
  split_plot(
    by = dow,
    ncol = 3, nrow = 3, guides = "collect"
  )

##
tidyplots::tidyplot(dow,
  colour = tip,
  data = taxi_modified
) %>%
  add_barstack_absolute() %>%
  add_title("Plot 4D: Counts of Tips by Day of Week and Month") %>%
  adjust_size(height = 50, width = 80, unit = "mm") %>%
  adjust_colors(colors_discrete_friendly) %>%
  adjust_x_axis(rotate_labels = 45) %>%
  split_plot(
    by = hour,
    ncol = 3, nrow = 8, guides = "collect"
  )

tinyplot-4

To be Coded.

web-r-4

Business Insights-4

• Note: We were using fill = ~ tip here! Why is that a good idea?
• tips vs hour: There are always more people who tip than those who do not. Of course there are fewer trips during the early morning hours and the late night hours, based on the very small bar-pairs we see at those times
• tips vs dow: Except for Sunday, the tip count patterns (Yes/No) look similar across all days.
• tips vs month: We have data for 4 months only. Again, the tip count patterns (Yes/No) look similar across all months. Perhaps slightly fewer trips in Jan, when it is cold in Chicago and people may not go out much.
• tips vs dow vs month: Very similar counts for tips(Yes/No) across day-of-week and month.

Bar Plot Extras

gf-bar and gf-col

Note also that gf_bar/geom_bar takes only ONE variable (for the x-axis), whereas gf_col/geom_col needs both X and Y variables since it simply plots columns. Both are useful!

And we can plot Proportions and Percentages too!

We have already seen gf_props in our two case studies above. Also check out gf_percents ! These are both very useful ggformula functions!

theme_set(new = theme_custom())

gf_props(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = "dodge"
) %>%
  gf_labs(
    title = "Plotting Proportions using gf_props",
    subtitle = "Option = dodge"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

gf_props(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = "fill"
) %>%
  gf_labs(
    title = "Plotting Proportions using gf_props",
    subtitle = "Option = fill"
  ) %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

theme_set(new = theme_custom())

gf_percents(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = "dodge"
) %>%
  gf_refine(
    scale_y_continuous(
      labels = scales::label_percent(scale = 1)
    )
  ) %>%
  gf_labs(title = "Plotting Percentages using gf_percents") %>%
  gf_refine(scale_fill_brewer(palette = "Set1"))

Are the Differences in Proportion Significant?

When we see situations such as this, where data has one or more Qual variables that are binary(Yes/No), we are always interested in whether these proportions of Yes/No are really different, or if we are just seeing the result of random chance. This is usually mechanized by a Stat Test called a Single Proportion Test or, when we have more than one, a Multiple Proportion Test.

Your Turn

Datasets

1. Click on the Dataset Icon above, and unzip that archive. Try to make Bar plots with each of them, using one or more Qual variables.

2. A dataset from calmcode.io https://calmcode.io/datasets.html

3. AiRbnb Price Data on the French Riviera:

4. Apartment price vs ground living area:

5. Fertility: This rather large and interesting Fertility related dataset from https://vincentarelbundock.github.io/Rdatasets/csv/AER/Fertility.csv

glimpse / skim / inspect the dataset in each case, state that Data Dictionary, and develop a set of Questions that can be answered by appropriate stat measures, or by using a chart to show the distribution.

Wait, But Why?

• Always count your chickens count your data before you model or infer!
• Counts first give you an absolute sense of how much data you have.
• Counts by different Qual variables give you a sense of the combinations you have in your data: \((Male/Female) * (Income-Status) * (Old/Young) * (Urban/Rural)\) (Say 2 * 3 * 2 * 2 = 24 combinations of data)
• Counts then give an idea whether your data is lop-sided: do you have too many observations of one category(level) and too few of another category(level) in a given Qual variable?
• Balance is important in order to draw decent inferences
• And for ML algorithms, to train them properly.
• Since the X-axis in bar charts is Qualitative (the bars don’t touch, remember!) it is possible to sort the bars at will, based on the levels within the Qualitative variables. See the approx Zipf’s Law distribution for the English alphabet below:

Figure 6: Zipf’s Law

In Figure 6, the letters of the alphabet are “levels” within a Qualitative variable, and these levels have been sorted based on the frequency or count! This is what Sherlock Holmes might have done, or the method how they cracked the code to the treasure in this story.

Conclusion

• Qualitative data variables can be plotted as counts, using Bar Charts
• gf_col and gf_bar provide Bar charts; gf_bar performs counts internally, whereas gf_col requires pre-counted data.
• Using facets allows us to view counts of one Qual variable split over two other Qual variables

AI Generated Summary and Podcast

This text excerpt focuses on bar charts and histograms as visualization tools for qualitative and quantitative data, respectively. It walks the reader through the creation of bar charts using the R programming language, illustrating the concept through a case study using the Chicago taxi rides dataset. The author explores various scenarios and questions related to taxi tipping, such as the frequency of tips and their dependence on trip locality, company, hour of the day, and day of the week. Finally, the excerpt highlights the importance of understanding data counts before undertaking data modeling or inference, emphasizing the role of bar charts in revealing data distribution and potential imbalances.

References

1. Daniel Kaplan and Randall Pruim. ggformula: Formula Interface for ggplot2 (full version). https://www.mosaic-web.org/ggformula/articles/pkgdown/ggformula-long.html
2. Winston Chang (2024). R Graphics Cookbook. https://r-graphics.org

R Package Citations

Package	Version	Citation
ggformula	0.12.0	Kaplan and Pruim (2023)
mosaic	1.9.1	Pruim, Kaplan, and Horton (2017)
tidyplots	0.3.1	Engler (2025)
tidyverse	2.0.0	Wickham et al. (2019)
tinyplot	0.4.1	McDermott, Arel-Bundock, and Zeileis (2025)

Engler, Jan Broder. 2025. “Tidyplots Empowers Life Scientists with Easy Code-Based Data Visualization.” iMeta, e70018. https://doi.org/10.1002/imt2.70018.

Kaplan, Daniel, and Randall Pruim. 2023. ggformula: Formula Interface to the Grammar of Graphics. https://doi.org/10.32614/CRAN.package.ggformula.

McDermott, Grant, Vincent Arel-Bundock, and Achim Zeileis. 2025. tinyplot: Lightweight Extension of the Base r Graphics System. https://doi.org/10.32614/CRAN.package.tinyplot.

Pruim, Randall, Daniel T Kaplan, and Nicholas J Horton. 2017. “The Mosaic Package: Helping Students to ‘Think with Data’ Using r.” The R Journal 9 (1): 77–102. https://journal.r-project.org/archive/2017/RJ-2017-024/index.html.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.